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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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Singh S, Pandey AK, Prajapati VK. From genome to clinic: The power of translational bioinformatics in improving human health. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:1-25. [PMID: 38448133 DOI: 10.1016/bs.apcsb.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.
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Affiliation(s)
- Satyendra Singh
- Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan, India
| | - Anurag Kumar Pandey
- College of Biotechnology, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, Uttar Pradesh, India
| | - Vijay Kumar Prajapati
- Department of Biochemistry, University of Delhi South Campus, Dhaula Kuan, New Delhi, India.
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Mou SI, Sultana T, Chatterjee D, Faruk MO, Hosen MI. Comprehensive characterization of coding and non-coding single nucleotide polymorphisms of the Myoneurin (MYNN) gene using molecular dynamics simulation and docking approaches. PLoS One 2024; 19:e0296361. [PMID: 38165846 PMCID: PMC10760682 DOI: 10.1371/journal.pone.0296361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 12/11/2023] [Indexed: 01/04/2024] Open
Abstract
Genome-wide association studies (GWAS) identified a coding single nucleotide polymorphism, MYNN rs10936599, at chromosome 3q. MYNN gene encodes myoneurin protein, which has been associated with several cancer pathogenesis and disease development processes. However, there needed to be a more detailed characterization of this polymorphism's (and other coding and non-coding polymorphisms) structural, functional, and molecular impact. The current study addressed this gap and analyzed different properties of rs10936599 and non-coding SNPs of MYNN via a thorough computational method. The variant, rs10936599, was predicted functionally deleterious by nine functionality prediction approaches, like SIFT, PolyPhen-2, and REVEL, etc. Following that, structural modifications were estimated through the HOPE server and Mutation3D. Moreover, the mutation was found in a conserved and active residue, according to ConSurf and CPORT. Further, the secondary structures were predicted, followed by tertiary structures, and there was a significant deviation between the native and variant models. Similarly, molecular simulation also showed considerable differences in the dynamic pattern of the wildtype and mutant structures. Molecular docking revealed that the variant binds with better docking scores with ligand NOTCH2. In addition to that, non-coding SNPs located at the MYNN locus were retrieved from the ENSEMBL database. These were found to disrupt the transcription factor binding regulatory regions; nonetheless, only two affect miRNA target sites. Again, eight non-coding variants were detected in the testes with normalized expression, whereas HaploReg v4.1 unveiled annotations for non-coding variants. In summary, in silico comprehensive characterization of coding and non-coding single nucleotide polymorphisms of MYNN gene will assist researchers to work on MYNN gene and establish their association with certain types of cancers.
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Affiliation(s)
- Sadia Islam Mou
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Tamanna Sultana
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Dipankor Chatterjee
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Md. Omar Faruk
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Md. Ismail Hosen
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
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Manuel AM, Dai Y, Jia P, Freeman LA, Zhao Z. A gene regulatory network approach harmonizes genetic and epigenetic signals and reveals repurposable drug candidates for multiple sclerosis. Hum Mol Genet 2023; 32:998-1009. [PMID: 36282535 PMCID: PMC9991005 DOI: 10.1093/hmg/ddac265] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/14/2022] [Accepted: 10/20/2022] [Indexed: 02/02/2023] Open
Abstract
Multiple sclerosis (MS) is a complex dysimmune disorder of the central nervous system. Genome-wide association studies (GWAS) have identified 233 genetic variations associated with MS at the genome-wide significant level. Epigenetic studies have pinpointed differentially methylated CpG sites in MS patients. However, the interplay between genetic risk factors and epigenetic regulation remains elusive. Here, we employed a network model to integrate GWAS summary statistics of 14 802 MS cases and 26 703 controls with DNA methylation profiles from 140 MS cases and 139 controls and the human interactome. We identified differentially methylated genes by aggregating additive effects of differentially methylated CpG sites within promoter regions. We reconstructed a gene regulatory network (GRN) using literature-curated transcription factor knowledge. Colocalization of the MS GWAS and methylation quantitative trait loci (mQTL) was performed to assess the GRN. The resultant MS-associated GRN highlighted several single nucleotide polymorphisms with GWAS-mQTL colocalization: rs6032663, rs6065926 and rs2024568 of CD40 locus, rs9913597 of STAT3 locus, and rs887864 and rs741175 of CIITA locus. Moreover, synergistic mQTL and expression QTL signals were identified in CD40, suggesting gene expression alteration was likely induced by epigenetic changes. Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA) indicated that the GRN was enriched in T follicular helper cells (P-value = 0.0016). Drug target enrichment analysis of annotations from the Therapeutic Target Database revealed the GRN was also enriched with drug target genes (P-value = 3.89 × 10-4), revealing repurposable candidates for MS treatment. These candidates included vorinostat (HDAC1 inhibitor) and sivelestat (ELANE inhibitor), which warrant further investigation.
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Affiliation(s)
- Astrid M Manuel
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Leorah A Freeman
- Department of Neurology, Dell Medical School, The University of Texas, Austin, TX 78712, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA
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Casotti MC, Meira DD, Alves LNR, Bessa BGDO, Campanharo CV, Vicente CR, Aguiar CC, Duque DDA, Barbosa DG, dos Santos EDVW, Garcia FM, de Paula F, Santana GM, Pavan IP, Louro LS, Braga RFR, Trabach RSDR, Louro TS, de Carvalho EF, Louro ID. Translational Bioinformatics Applied to the Study of Complex Diseases. Genes (Basel) 2023; 14:419. [PMID: 36833346 PMCID: PMC9956936 DOI: 10.3390/genes14020419] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/29/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023] Open
Abstract
Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment of cancer. An integrative literature review was carried out, obtaining articles through several websites, among them: PUBMED, Science Direct, NCBI-PMC, Scientific Electronic Library Online (SciELO), and Google Academic, published in English, Spanish, and Portuguese, indexed in the referred databases and answering the following guiding question: "How does TBI provide a scientific understanding of complex diseases?" An additional effort is aimed at the dissemination, inclusion, and perpetuation of TBI knowledge from the academic environment to society, helping the study, understanding, and elucidating of complex disease mechanics and their treatment.
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Affiliation(s)
- Matheus Correia Casotti
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Dummer Meira
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Lyvia Neves Rebello Alves
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Camilly Victória Campanharo
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Creuza Rachel Vicente
- Departamento de Medicina Social, Universidade Federal do Espírito Santo, Vitória 29040-090, Espírito Santo, Brazil
| | - Carla Carvalho Aguiar
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Daniel de Almeida Duque
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Gonçalves Barbosa
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Fernanda Mariano Garcia
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Flávia de Paula
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Gabriel Mendonça Santana
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Isabele Pagani Pavan
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Luana Santos Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Furlani Rocon Braga
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Silva dos Reis Trabach
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Thomas Santos Louro
- Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Vitória 29027-502, Espírito Santo, Brazil
| | - Elizeu Fagundes de Carvalho
- Instituto de Biologia Roberto Alcantara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro 20551-030, Rio de Janeiro, Brazil
| | - Iúri Drumond Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
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Bichel-Findlay J, Koch S, Mantas J, Abdul SS, Al-Shorbaji N, Ammenwerth E, Baum A, Borycki EM, Demiris G, Hasman A, Hersh W, Hovenga E, Huebner UH, Huesing ES, Kushniruk A, Hwa Lee K, Lehmann CU, Lillehaug SI, Marin HF, Marschollek M, Martin-Sanchez F, Merolli M, Nishimwe A, Saranto K, Sent D, Shachak A, Udayasankaran JG, Were MC, Wright G. Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics: Second Revision. Int J Med Inform 2023; 170:104908. [PMID: 36502741 DOI: 10.1016/j.ijmedinf.2022.104908] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The purpose of educational recommendations is to assist in establishing courses and programs in a discipline, to further develop existing educational activities in the various nations, and to support international initiatives for collaboration and sharing of courseware. The International Medical Informatics Association (IMIA) has published two versions of its international recommendations in biomedical and health informatics (BMHI) education, initially in 2000 and revised in 2010. Given the recent changes to the science, technology, the needs of the healthcare systems, and the workforce of BMHI, a revision of the recommendations is necessary. OBJECTIVE The aim of these updated recommendations is to support educators in developing BMHI curricula at different education levels, to identify essential skills and competencies for certification of healthcare professionals and those working in the field of BMHI, to provide a tool for evaluators of academic BMHI programs to compare and accredit the quality of delivered programs, and to motivate universities, organizations, and health authorities to recognize the need for establishing and further developing BMHI educational programs. METHOD An IMIA taskforce, established in 2017, updated the recommendations. The taskforce included representatives from all IMIA regions, with several having been involved in the development of the previous version. Workshops were held at different IMIA conferences, and an international Delphi study was performed to collect expert input on new and revised competencies. RESULTS Recommendations are provided for courses/course tracks in BMHI as part of educational programs in biomedical and health sciences, health information management, and informatics/computer science, as well as for dedicated programs in BMHI (leading to bachelor's, master's, or doctoral degree). The educational needs are described for the roles of BMHI user, BMHI generalist, and BMHI specialist across six domain areas - BMHI core principles; health sciences and services; computer, data and information sciences; social and behavioral sciences; management science; and BMHI specialization. Furthermore, recommendations are provided for dedicated educational programs in BMHI at the level of bachelor's, master's, and doctoral degrees. These are the mainstream academic programs in BMHI. In addition, recommendations for continuing education, certification, and accreditation procedures are provided. CONCLUSION The IMIA recommendations reflect societal changes related to globalization, digitalization, and digital transformation in general and in healthcare specifically, and center on educational needs for the healthcare workforce, computer scientists, and decision makers to acquire BMHI knowledge and skills at various levels. To support education in BMHI, IMIA offers accreditation of quality BMHI education programs. It supports information exchange on programs and courses in BMHI through its Working Group on Health and Medical Informatics Education.
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Affiliation(s)
| | - Sabine Koch
- Health Informatics Centre, Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Sweden
| | - John Mantas
- Health Informatics Lab, School of Health Sciences, National and Kapodistrian University of Athens, Greece
| | - Shabbir S Abdul
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taiwan
| | | | - Elske Ammenwerth
- UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
| | - Analia Baum
- Hospital Italiano de Buenos Aires, Health Informatics Department, Argentina
| | | | - George Demiris
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Arie Hasman
- Department of Medical Informatics Amsterdam UMC, location AMC, The Netherlands
| | - William Hersh
- Department of Medical Informatics & Clinical Epidemiology, School of Medicine, Oregon Health & Science University, United States
| | - Evelyn Hovenga
- Digital Health, Australian Catholic University, Australia
| | - Ursula H Huebner
- Hochschule Osnabrueck - University AS Osnabrueck, Department of Business Management and Social Sciences, Germany
| | | | - Andre Kushniruk
- School of Health Information Science, University of Victoria, Canada
| | - Kye Hwa Lee
- Department of Information Medicine, Asan Medical Center and University of Ulsan College of Medicine, South Korea
| | - Christoph U Lehmann
- Clinical Informatics Center, University of Texas Southwestern Medical Center, United States
| | | | | | - Michael Marschollek
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Germany
| | | | - Mark Merolli
- Department of Physiotherapy, School of Health Sciences, Centre for Health, Exercise and Sports Medicine, Centre for Digital Transformation of Health, The University of Melbourne, Australia
| | - Aurore Nishimwe
- Health Informatics Program, College of Medicine and Health Sciences, University of Rwanda, Rwanda
| | - Kaija Saranto
- Health and Human Services Informatics, University of Eastern Finland, Finland
| | - Danielle Sent
- Department of Medical Informatics Amsterdam UMC, location AMC, The Netherlands
| | - Aviv Shachak
- Institute of Health Policy, Management and Evaluation (Dalla Lana School of Public Health), University of Toronto, Canada
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Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine. Int J Mol Sci 2022; 24:ijms24010004. [PMID: 36613446 PMCID: PMC9819745 DOI: 10.3390/ijms24010004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/16/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.
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Zhou H, Tian J, Sun H, Fu J, Lin N, Yuan D, Zhou L, Xia M, Sun L. Systematic Identification of Genomic Markers for Guiding Iron Oxide Nanoparticles in Cervical Cancer Based on Translational Bioinformatics. Int J Nanomedicine 2022; 17:2823-2841. [PMID: 35791307 PMCID: PMC9250777 DOI: 10.2147/ijn.s361483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose Magnetic iron oxide nanoparticle (MNP) drug delivery system is a novel promising therapeutic option for cancer treatment. Material issues such as fabrication and functionalized modification have been investigated; however, pharmacologic mechanisms of bare MNPs inside cancer cells remain obscure. This study aimed to explore a systems pharmacology approach to understand the reaction of the whole cell to MNPs and suggest drug selection in MNP delivery systems to exert synergetic or additive anti-cancer effects. Methods HeLa and SiHa cell lines were used to estimate the properties of bare MNPs in cervical cancer through 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) and enzyme activity assays and cellular fluorescence imaging. A systems pharmacology approach was utilized by combining bioinformatics data mining with clinical data analysis and without a predefined hypothesis. Key genes of the MNP onco-pharmacologic mechanism in cervical cancer were identified and further validated through transcriptome analysis with quantitative reverse transcription PCR (qRT-PCR). Results Low cytotoxic activity and cell internalization of MNP in HeLa and SiHa cells were observed. Lysosomal function was found to be impaired after MNP treatment. Protein tyrosine kinase 2 beta (PTK2B), liprin-alpha-4 (PPFIA4), mothers against decapentaplegic homolog 7 (SMAD7), and interleukin (IL) 1B were identified as key genes relevant for MNP pharmacology, clinical features, somatic mutation, and immune infiltration. The four key genes also exhibited significant correlations with the lysosome gene set. The qRT-PCR results showed significant alterations in the expression of the four key genes after MNP treatment in HeLa and SiHa cells. Conclusion Our research suggests that treatment of bare MNPs in HeLa and SiHa cells induced significant expression changes in PTK2B, PPFIA4, SMAD7, and IL1B, which play crucial roles in cervical cancer development and progression. Interactions of the key genes with specific anti-cancer drugs must be considered in the rational design of MNP drug delivery systems.
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Affiliation(s)
- Haohan Zhou
- Key Laboratory of Pathobiology, Ministry of Education, Department of Pathophysiology, College of Basic Medical Sciences, Jilin University, Changchun, 130021, People's Republic of China.,Department of Orthopaedic Oncology, Changzheng Hospital, Second Military Medical University, Shanghai, 200000, People's Republic of China
| | - Jiayi Tian
- First Hospital, Jilin University, Changchun, 130021, People's Republic of China
| | - Hongyu Sun
- Key Laboratory of Pathobiology, Ministry of Education, Department of Pathophysiology, College of Basic Medical Sciences, Jilin University, Changchun, 130021, People's Republic of China
| | - Jiaying Fu
- Key Laboratory of Pathobiology, Ministry of Education, Department of Pathophysiology, College of Basic Medical Sciences, Jilin University, Changchun, 130021, People's Republic of China
| | - Nan Lin
- Key Laboratory of Pathobiology, Ministry of Education, Department of Pathophysiology, College of Basic Medical Sciences, Jilin University, Changchun, 130021, People's Republic of China
| | - Danni Yuan
- Key Laboratory of Pathobiology, Ministry of Education, Department of Pathophysiology, College of Basic Medical Sciences, Jilin University, Changchun, 130021, People's Republic of China
| | - Li Zhou
- First Hospital, Jilin University, Changchun, 130021, People's Republic of China
| | - Meihui Xia
- First Hospital, Jilin University, Changchun, 130021, People's Republic of China
| | - Liankun Sun
- Key Laboratory of Pathobiology, Ministry of Education, Department of Pathophysiology, College of Basic Medical Sciences, Jilin University, Changchun, 130021, People's Republic of China
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Singla RK, Joon S, Shen L, Shen B. Translational Informatics for Natural Products as Antidepressant Agents. Front Cell Dev Biol 2022; 9:738838. [PMID: 35127696 PMCID: PMC8811306 DOI: 10.3389/fcell.2021.738838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.
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Affiliation(s)
- Rajeev K. Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Shikha Joon
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Li Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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Auerbach A, Fihn SD. Discovery, Learning, and Experimentation With Artificial Intelligence-Based Tools at the Point of Care-Perils and Opportunity. JAMA Netw Open 2021; 4:e211474. [PMID: 33704470 DOI: 10.1001/jamanetworkopen.2021.1474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Andrew Auerbach
- Department of Medicine, University of California, San Francisco, San Francisco
| | - Stephan D Fihn
- Department of Medicine, University of Washington, Seattle
- Deputy Editor, JAMA Network Open
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Melas M, Subbiah S, Saadat S, Rajurkar S, McDonnell KJ. The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations. J Clin Med 2020; 9:E2125. [PMID: 32640668 PMCID: PMC7408957 DOI: 10.3390/jcm9072125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 06/28/2020] [Accepted: 07/02/2020] [Indexed: 12/15/2022] Open
Abstract
Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions.
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Affiliation(s)
- Marilena Melas
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA;
| | - Shanmuga Subbiah
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Glendora, CA 91741, USA;
| | - Siamak Saadat
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Colton, CA 92324, USA;
| | - Swapnil Rajurkar
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Upland, CA 91786, USA;
| | - Kevin J. McDonnell
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA 91010, USA
- Center for Precision Medicine, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
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12
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Watkins M, Viernes B, Nguyen V, Rojas Mezarina L, Silva Valencia J, Borbolla D. Translating Social Determinants of Health into Standardized Clinical Entities. Stud Health Technol Inform 2020; 270:474-478. [PMID: 32570429 DOI: 10.3233/shti200205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Social determinants of health (SDH) are a valuable source of health information which still are not fully utilized in the clinical space. Knowing that a certain patient has trouble finding transportation, has a potentially hazardous relationship with a family member or close relative, is currently unemployed, or various other social factors would allow providers to tailor treatment plans in a way to best help that patient. However, these SDH must be gathered, represented, and stored in a standardized way before they can be leveraged by informatics tools designed for health providers. This process of translating SDH to standardized clinical entities includes two main steps. The first is a collaborative effort to establish an ontology of medical terminology codes (i.e., ICD, SNOMED, LOINC, etc.) which can be used to uniformly represent SDH as coded concepts. The second is a collaborative effort to use the FHIR standard to create profiles and extensions which will allow FHIR resources to be used to store the coded SDH as clinical entities. Each of these steps has their own complexities that must be considered and accounted for in future efforts to create interoperable clinical informatics solutions which utilize SDH.
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Affiliation(s)
- Michael Watkins
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
| | - Benjamin Viernes
- Population Health Sciences, 295 Chipeta Way, University of Utah, Salt Lake City, Utah 84108
| | | | - Leonardo Rojas Mezarina
- Information Systems Office, Jr. Capac Yupanqui 1400, National Institute of Health, Lima, Perú 15072.,Telesalud Unit, Av Grau 755, Universidad Nacional Mayor de San Marcos, School of Medicine, Lima, Perú 15001
| | - Javier Silva Valencia
- Telesalud Unit, Av Grau 755, Universidad Nacional Mayor de San Marcos, School of Medicine, Lima, Perú 15001
| | - Damian Borbolla
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
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13
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Mustafa MI, Murshed NS, Abdelmoneim AH, Abdelmageed MI, Elfadol NM, Makhawi AM. Extensive In Silico Analysis of ATL1 Gene : Discovered Five Mutations That May Cause Hereditary Spastic Paraplegia Type 3A. SCIENTIFICA 2020; 2020:8329286. [PMID: 32322428 PMCID: PMC7140133 DOI: 10.1155/2020/8329286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 01/31/2020] [Accepted: 02/21/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Hereditary spastic paraplegia type 3A (SPG3A) is a neurodegenerative disease inherited type of Hereditary spastic paraplegia (HSP). It is the second most frequent type of HSP which is characterized by progressive bilateral and mostly symmetric spasticity and weakness of the legs. SPG3A gene mutations and the phenotype-genotype correlations have not yet been recognized. The aim of this work was to categorize the most damaging SNPs in ATL1 gene and to predict their impact on the functional and structural levels by several computational analysis tools. METHODS The raw data of ATL1 gene were retrieved from dbSNP database and then run into numerous computational analysis tools. Additionally; we submitted the common six deleterious outcomes from the previous functional analysis tools to I-mutant 3.0 and MUPro, respectively, to investigate their effect on the structural level. The 3D structure of ATL1 was predicted by RaptorX and modeled using UCSF Chimera to compare the differences between the native and the mutant amino acids. RESULTS Five nsSNPs out of 249 were classified as the most deleterious (rs746927118, rs979765709, rs119476049, rs864622269, and rs1242753115). CONCLUSIONS In this study, the impact of nsSNPs in the ATL1 gene was investigated by various in silico tools that revealed five nsSNPs (V67F, T120I, R217Q, R495W, and G504E) are deleterious SNPs, which have a functional impact on ATL1 protein and, therefore, can be used as genomic biomarkers specifically before 4 years of age; also, it may play a key role in pharmacogenomics by evaluating drug response for this disabling disease.
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Affiliation(s)
| | - Naseem S. Murshed
- Department of Microbiology, International University of Africa, Khartoum, Sudan
| | | | | | - Nafisa M. Elfadol
- Department of Microbiology, National Ribat University, Khartoum, Sudan
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14
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Tambuyzer E, Vandendriessche B, Austin CP, Brooks PJ, Larsson K, Miller Needleman KI, Valentine J, Davies K, Groft SC, Preti R, Oprea TI, Prunotto M. Therapies for rare diseases: therapeutic modalities, progress and challenges ahead. Nat Rev Drug Discov 2019; 19:93-111. [PMID: 31836861 DOI: 10.1038/s41573-019-0049-9] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2019] [Indexed: 12/26/2022]
Abstract
Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.
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Affiliation(s)
- Erik Tambuyzer
- BioPontis Alliance for Rare Diseases Foundation fup/son, Brussels, Belgium. .,BioPontis Alliance Rare Disease Foundation, Inc, Raleigh, NC, USA.
| | - Benjamin Vandendriessche
- Byteflies, Antwerp, Belgium.,Department of Electrical, Computer, and Systems Engineering (ECSE), Case Western Reserve University, Cleveland, OH, USA
| | - Christopher P Austin
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Philip J Brooks
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Kristina Larsson
- Orphan Medicines Office, European Medicines Agency, Amsterdam, Netherlands
| | | | | | - Kay Davies
- MDUK Oxford Neuromuscular Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Stephen C Groft
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Robert Preti
- Hitachi Chemical Regenerative Medicine Business Sector, Allendale, NJ, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Albuquerque, Albuquerque, NM, USA.,UNM Comprehensive Cancer Center, University of New Mexico Health Science Center, Albuquerque, NM, USA
| | - Marco Prunotto
- School of Pharmaceutical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland.
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15
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Azad RK, Shulaev V. Metabolomics technology and bioinformatics for precision medicine. Brief Bioinform 2019; 20:1957-1971. [PMID: 29304189 PMCID: PMC6954408 DOI: 10.1093/bib/bbx170] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 11/29/2017] [Indexed: 12/14/2022] Open
Abstract
Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.
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Affiliation(s)
| | - Vladimir Shulaev
- Corresponding author: Vladimir Shulaev, Department of Biological Sciences, BioDiscovery Institute, University of North Texas, Denton, TX 76210, USA. Tel.: 940-369-5368; Fax: 940-565-3821; E-mail:
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16
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Djulbegovic MB, Uversky VN. Expanding the understanding of the heterogeneous nature of melanoma with bioinformatics and disorder-based proteomics. Int J Biol Macromol 2019; 150:1281-1293. [PMID: 31743721 DOI: 10.1016/j.ijbiomac.2019.10.139] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 09/19/2019] [Accepted: 10/15/2019] [Indexed: 01/07/2023]
Abstract
The past few decades show that incidences of melanoma are on the rise. The risk associated with this disease is an interplay between genetic and host factors and sun exposure. While scientific progress in the treatment of melanoma is remarkable, additional research is needed to improve patient outcomes and to better understand the heterogenous nature of this disease. Fortunately, as the clinical community enters the era of "big data" and personalized medicine, the rise of bioinformatics that stems from recent advances in high throughout profiling of biological information offers potential for innovative treatment options. This study aims to provide an example of the usefulness of bioinformatics and disorder-based proteomics to identify the molecular pathway in melanoma, garner information on selected proteins from this pathway and uncover their intrinsically disordered proteins regions (IDPRs) and investigate functionality implicated in these IDPRs. The present study provides a new look at the melanoma heterogeneity and suggests that, in addition to the well-established genetic heterogeneity of melanoma, there is another level of heterogeneity that lies within the conformational ensembles that stem from intrinsic disorder in melanoma-related proteins. The hope is that these insights will inspire future drug discovery campaigns.
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Affiliation(s)
- Mak B Djulbegovic
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
| | - Vladimir N Uversky
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA; USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA; Protein Research Group, Institute for Biological Instrumentation of the Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russia.
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17
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Mustafa MI, Mohammed ZO, Murshed NS, Elfadol NM, Abdelmoneim AH, Hassan MA. In Silico Genetics Revealing 5 Mutations in CEBPA Gene Associated With Acute Myeloid Leukemia. Cancer Inform 2019; 18:1176935119870817. [PMID: 31621694 PMCID: PMC6777061 DOI: 10.1177/1176935119870817] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Accepted: 07/30/2019] [Indexed: 12/11/2022] Open
Abstract
Background: Acute myeloid leukemia (AML) is an extremely heterogeneous malignant
disorder; AML has been reported as one of the main causes of death in
children. The objective of this work was to classify the most deleterious
mutation in CCAAT/enhancer-binding protein-alpha (CEBPA)
and to predict their influence on the functional, structural, and expression
levels by various Bioinformatics analysis tools. Methods: The single nucleotide polymorphisms (SNPs) were claimed from the National
Center for Biotechnology Information (NCBI) database and then submitted into
various functional analysis tools, which were done to predict the influence
of each SNP, followed by structural analysis of modeled protein followed by
predicting the mutation effect on energy stability; the most damaging
mutations were chosen for additional investigation by Mutation3D, Project
hope, ConSurf, BioEdit, and UCSF Chimera tools. Results: A total of 5 mutations out of 248 were likely to be responsible for the
structural and functional variations in CEBPA protein, whereas in the
3′-untranslated region (3′-UTR) the result showed that among 350 SNPs in the
3′-UTR of CEBPA gene, about 11 SNPs were predicted. Among
these 11 SNPs, 65 alleles disrupted a conserved miRNA site and 22 derived
alleles created a new site of miRNA. Conclusions: In this study, the impact of functional mutations in the CEBPA gene was
investigated through different bioinformatics analysis techniques, which
determined that R339W, R288P, N292S, N292T, and D63N are pathogenic
mutations that have a possible functional and structural influence,
therefore, could be used as genetic biomarkers and may assist in genetic
studies with a special consideration of the large heterogeneity of AML.
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Affiliation(s)
- Mujahed I Mustafa
- Department of Biotechnology, Africa City of Technology, Khartoum North, Sudan
| | - Zainab O Mohammed
- Department of Haematology, Ribat University Hospital, Khartoum, Sudan
| | - Naseem S Murshed
- Department of Biotechnology, Africa City of Technology, Khartoum North, Sudan
| | - Nafisa M Elfadol
- Department of Biotechnology, Africa City of Technology, Khartoum North, Sudan
| | | | - Mohamed A Hassan
- Department of Biotechnology, Africa City of Technology, Khartoum North, Sudan
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18
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Emam I, Elyasigomari V, Matthews A, Pavlidis S, Rocca-Serra P, Guitton F, Verbeeck D, Grainger L, Borgogni E, Del Giudice G, Saqi M, Houston P, Guo Y. PlatformTM, a standards-based data custodianship platform for translational medicine research. Sci Data 2019; 6:149. [PMID: 31409798 PMCID: PMC6692384 DOI: 10.1038/s41597-019-0156-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 07/25/2019] [Indexed: 12/20/2022] Open
Abstract
Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the 'manageability' of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.
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Affiliation(s)
- Ibrahim Emam
- Data Science Institute, Imperial College London, London, UK.
| | | | - Alex Matthews
- Clinical Research Centre, University of Surrey, Guildford, UK
| | | | | | | | | | | | | | | | - Mansoor Saqi
- Data Science Institute, Imperial College London, London, UK
| | - Paul Houston
- CDISC, Clinical Data Interchange Standards Consortium and CDISC EU Foundation, London, UK
| | - Yike Guo
- Data Science Institute, Imperial College London, London, UK
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19
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Krumm N, Shirts BH. Technical, Biological, and Systems Barriers for Molecular Clinical Decision Support. Clin Lab Med 2019; 39:281-294. [PMID: 31036281 DOI: 10.1016/j.cll.2019.01.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Genome-enabled or molecular clinical decision support (CDS) systems provide unique advantages for the clinical use of genomic data; however, their implementation is complicated by technical, biological, and systemic barriers. This article reviews the substantial technical progress that has been made in the past decade and finds that the underlying biological limitations of genomics as well as systemic barriers to adoption of molecular CDS have been comparatively underestimated. A hybrid consultative CDS system, which integrates a genomics consultant into an active CDS system, may provide an interim path forward.
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Affiliation(s)
- Niklas Krumm
- Department of Laboratory Medicine, University of Washington, Box 357110, 1959 Northeast Pacific Street, NW120, Seattle, WA 98195-7110, USA.
| | - Brian H Shirts
- Department of Laboratory Medicine, University of Washington, Box 357110, 1959 Northeast Pacific Street, NW120, Seattle, WA 98195-7110, USA
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20
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Swindell WR, Bojanowski K, Kindy MS, Chau RMW, Ko D. GM604 regulates developmental neurogenesis pathways and the expression of genes associated with amyotrophic lateral sclerosis. Transl Neurodegener 2018; 7:30. [PMID: 30524706 PMCID: PMC6276193 DOI: 10.1186/s40035-018-0135-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 10/21/2018] [Indexed: 12/11/2022] Open
Abstract
Background Amyotrophic lateral sclerosis (ALS) is currently an incurable disease without highly effective pharmacological treatments. The peptide drug GM604 (GM6 or Alirinetide) was developed as a candidate ALS therapy, which has demonstrated safety and good drug-like properties with a favorable pharmacokinetic profile. GM6 is hypothesized to bolster neuron survival through the multi-target regulation of developmental pathways, but mechanisms of action are not fully understood. Methods This study used RNA-seq to evaluate transcriptome responses in SH-SY5Y neuroblastoma cells following GM6 treatment (6, 24 and 48 h). Results We identified 2867 protein-coding genes with expression significantly altered by GM6 (FDR < 0.10). Early (6 h) responses included up-regulation of Notch and hedgehog signaling components, with increased expression of developmental genes mediating neurogenesis and axon growth. Prolonged GM6 treatment (24 and 48 h) altered the expression of genes contributing to cell adhesion and the extracellular matrix. GM6 further down-regulated the expression of genes associated with mitochondria, inflammatory responses, mRNA processing and chromatin organization. GM6-increased genes were located near GC-rich motifs interacting with C2H2 zinc finger transcription factors, whereas GM6-decreased genes were located near AT-rich motifs associated with helix-turn-helix homeodomain factors. Such motifs interacted with a diverse network of transcription factors encoded by GM6-regulated genes (STAT3, HOXD11, HES7, GLI1). We identified 77 ALS-associated genes with expression significantly altered by GM6 treatment (FDR < 0.10), which were known to function in neurogenesis, axon guidance and the intrinsic apoptosis pathway. Conclusions Our findings support the hypothesis that GM6 acts through developmental-stage pathways to influence neuron survival. Gene expression responses were consistent with neurotrophic effects, ECM modulation, and activation of the Notch and hedgehog neurodevelopmental pathways. This multifaceted mechanism of action is unique among existing ALS drug candidates and may be applicable to multiple neurodegenerative diseases. Electronic supplementary material The online version of this article (10.1186/s40035-018-0135-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- William R Swindell
- 1Heritage College of Osteopathic Medicine, Ohio University, Athens, OH USA
| | | | - Mark S Kindy
- 3Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL USA.,4James A. Haley VAMC, Tampa, FL USA
| | | | - Dorothy Ko
- Genervon Biopharmaceuticals LLC, Pasadena, CA USA
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21
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Shameer K, Nayarisseri A, Romero Duran FX, Gonzalez-Diaz H. Editorial: Improving Neuropharmacology using Big Data, Machine Learning and Computational Algorithms. Curr Neuropharmacol 2018; 15:1058-1061. [PMID: 29199918 PMCID: PMC5725537 DOI: 10.2174/1570159x1508171114113425] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Khader Shameer
- Institute of Next Generation Healthcare (INGH), Icahn Institute of Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Mount Sinai Health System, Manhattan, NY, USA
| | - Anuraj Nayarisseri
- Bioinformatics Research Laboratory, Eminent Biosciences, Vijaynagar, Indore-452010, Madhya Pradesh, India.,In silico Research Laboratory, Legene Biosciences, Vijaynagar, Indore-452010, Madhya Pradesh, India
| | | | - Humberto Gonzalez-Diaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Biscay, Spain.,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Biscay, Spain
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22
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Banerjee A. Challenges for learning health systems in the NHS. Case study: electronic health records in cardiology. Future Healthc J 2017; 4:193-197. [PMID: 31098470 PMCID: PMC6502575 DOI: 10.7861/futurehosp.4-3-193] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Electronic health records (EHRs) are at the centre of advances in health informatics, but also many other innovations in healthcare. However, there are still obstacles to implementation and realisation of the full potential of EHRs as there are with learning health systems (LHS). Cardiovascular disease, in the UK and globally, carries greater morbidity and mortality than any other disease. Therefore, planning and delivery of health services represent major costs to individuals and populations. Both the scale of disease burden and the growing role of technology in cardiology practice make analysis of experiences with EHRs in cardiology a useful lens through which to view achievements and gaps to date. In this article regarding LHS, EHRs in cardiology are used as a case study of LHS in the NHS.
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Affiliation(s)
- Amitava Banerjee
- Farr Institute of Health Informatics Research, University College London, London, UK
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23
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Kwon Y, Natori Y, Tanokura M. New approach to generating insights for aging research based on literature mining and knowledge integration. PLoS One 2017; 12:e0183534. [PMID: 28817730 PMCID: PMC5560588 DOI: 10.1371/journal.pone.0183534] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 08/05/2017] [Indexed: 01/01/2023] Open
Abstract
The proportion of the elderly population in most countries worldwide is increasing dramatically. Therefore, social interest in the fields of health, longevity, and anti-aging has been increasing as well. However, the basic research results obtained from a reductionist approach in biology and a bioinformatic approach in genome science have limited usefulness for generating insights on future health, longevity, and anti-aging-related research on a case by case basis. We propose a new approach that uses our literature mining technique and bioinformatics, which lead to a better perspective on research trends by providing an expanded knowledge base to work from. We demonstrate that our approach provides useful information that deepens insights on future trends which differs from data obtained conventionally, and this methodology is already paving the way for a new field in aging-related research based on literature mining. One compelling example of this is how our new approach can be a useful tool in drug repositioning.
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Affiliation(s)
- Yeondae Kwon
- Laboratory of Basic Science on Healthy Longevity, Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Yukikazu Natori
- Laboratory of Basic Science on Healthy Longevity, Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Masaru Tanokura
- Laboratory of Basic Science on Healthy Longevity, Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
- * E-mail:
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24
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Vamathevan J, Birney E. A Review of Recent Advances in Translational Bioinformatics: Bridges from Biology to Medicine. Yearb Med Inform 2017; 26:178-187. [PMID: 29063562 PMCID: PMC6239226 DOI: 10.15265/iy-2017-017] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Indexed: 11/24/2022] Open
Abstract
Objectives: To highlight and provide insights into key developments in translational bioinformatics between 2014 and 2016. Methods: This review describes some of the most influential bioinformatics papers and resources that have been published between 2014 and 2016 as well as the national genome sequencing initiatives that utilize these resources to routinely embed genomic medicine into healthcare. Also discussed are some applications of the secondary use of patient data followed by a comprehensive view of the open challenges and emergent technologies. Results: Although data generation can be performed routinely, analyses and data integration methods still require active research and standardization to improve streamlining of clinical interpretation. The secondary use of patient data has resulted in the development of novel algorithms and has enabled a refined understanding of cellular and phenotypic mechanisms. New data storage and data sharing approaches are required to enable diverse biomedical communities to contribute to genomic discovery. Conclusion: The translation of genomics data into actionable knowledge for use in healthcare is transforming the clinical landscape in an unprecedented way. Exciting and innovative models that bridge the gap between clinical and academic research are set to open up the field of translational bioinformatics for rapid growth in a digital era.
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Affiliation(s)
- J. Vamathevan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - E. Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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25
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Sitapati A, Kim H, Berkovich B, Marmor R, Singh S, El-Kareh R, Clay B, Ohno-Machado L. Integrated precision medicine: the role of electronic health records in delivering personalized treatment. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2017; 9. [PMID: 28207198 DOI: 10.1002/wsbm.1378] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Revised: 10/24/2016] [Accepted: 12/02/2016] [Indexed: 12/23/2022]
Abstract
Precision Medicine involves the delivery of a targeted, personalized treatment for a given patient. By harnessing the power of electronic health records (EHRs), we are increasingly able to practice precision medicine to improve patient outcomes. In this article, we introduce the scientific community at large to important building blocks for personalized treatment, such as terminology standards that are the foundation of the EHR and allow for exchange of health information across systems. We briefly review different types of clinical decision support (CDS) and present the current state of CDS, which is already improving the care patients receive with genetic profile-based tailored recommendations regarding diagnostic and treatment plans. We also report on limitations of current systems, which are slowly beginning to integrate new genomic data into patient records but still present many challenges. Finally, we discuss future directions and how the EHR can evolve to increase the capacity of the healthcare system in delivering Precision Medicine at the point of care. WIREs Syst Biol Med 2017, 9:e1378. doi: 10.1002/wsbm.1378 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Amy Sitapati
- Department of Medicine, UC San Diego, San Diego, CA, USA.,UC San Diego Health System, San Diego, CA, USA.,Department of Biomedical Informatics, UC San Diego, San Diego, CA, USA
| | - Hyeoneui Kim
- Department of Medicine, UC San Diego, San Diego, CA, USA.,UC San Diego Health System, San Diego, CA, USA.,Department of Biomedical Informatics, UC San Diego, San Diego, CA, USA
| | | | - Rebecca Marmor
- Department of Medicine, UC San Diego, San Diego, CA, USA.,UC San Diego Health System, San Diego, CA, USA.,Department of Biomedical Informatics, UC San Diego, San Diego, CA, USA
| | - Siddharth Singh
- Department of Medicine, UC San Diego, San Diego, CA, USA.,UC San Diego Health System, San Diego, CA, USA.,Department of Biomedical Informatics, UC San Diego, San Diego, CA, USA
| | - Robert El-Kareh
- Department of Medicine, UC San Diego, San Diego, CA, USA.,UC San Diego Health System, San Diego, CA, USA.,Department of Biomedical Informatics, UC San Diego, San Diego, CA, USA
| | - Brian Clay
- Department of Medicine, UC San Diego, San Diego, CA, USA.,UC San Diego Health System, San Diego, CA, USA.,Department of Biomedical Informatics, UC San Diego, San Diego, CA, USA
| | - Lucila Ohno-Machado
- Department of Medicine, UC San Diego, San Diego, CA, USA.,UC San Diego Health System, San Diego, CA, USA.,Department of Biomedical Informatics, UC San Diego, San Diego, CA, USA.,San Diego VA Health System
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26
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Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, Bernal-Delgado E, Blomberg N, Bock C, Conesa A, Del Signore S, Delogne C, Devilee P, Di Meglio A, Eijkemans M, Flicek P, Graf N, Grimm V, Guchelaar HJ, Guo YK, Gut IG, Hanbury A, Hanif S, Hilgers RD, Honrado Á, Hose DR, Houwing-Duistermaat J, Hubbard T, Janacek SH, Karanikas H, Kievits T, Kohler M, Kremer A, Lanfear J, Lengauer T, Maes E, Meert T, Müller W, Nickel D, Oledzki P, Pedersen B, Petkovic M, Pliakos K, Rattray M, I Màs JR, Schneider R, Sengstag T, Serra-Picamal X, Spek W, Vaas LAI, van Batenburg O, Vandelaer M, Varnai P, Villoslada P, Vizcaíno JA, Wubbe JPM, Zanetti G. Making sense of big data in health research: Towards an EU action plan. Genome Med 2016; 8:71. [PMID: 27338147 PMCID: PMC4919856 DOI: 10.1186/s13073-016-0323-y] [Citation(s) in RCA: 124] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.
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Affiliation(s)
- Charles Auffray
- European Institute for Systems Biology and Medicine, 1 avenue Claude Vellefaux, 75010, Paris, France.
- CIRI-UMR5308, CNRS-ENS-INSERM-UCBL, Université de Lyon, 50 avenue Tony Garnier, 69007, Lyon, France.
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362, Esch-sur-Alzette, Luxembourg.
| | - Inês Barroso
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - László Bencze
- Health Services Management Training Centre, Faculty of Health and Public Services, Semmelweis University, Kútvölgyi út 2, 1125, Budapest, Hungary
| | - Mikael Benson
- Centre for Personalised Medicine, Linköping University, 581 85, Linköping, Sweden
| | - Jay Bergeron
- Translational & Bioinformatics, Pfizer Inc., 300 Technology Square, Cambridge, MA, 02139, USA
| | - Enrique Bernal-Delgado
- Institute for Health Sciences, IACS - IIS Aragon, San Juan Bosco 13, 50009, Zaragoza, Spain
| | - Niklas Blomberg
- ELIXIR, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.2, 1090, Vienna, Austria
- Department of Laboratory Medicine, Medical University of Vienna, Lazarettgasse 14, AKH BT25.2, 1090, Vienna, Austria
- Max Planck Institute for Informatics, Campus E1 4, 66123, Saarbrücken, Germany
| | - Ana Conesa
- Príncipe Felipe Research Center, C/ Eduardo Primo Yúfera 3, 46012, Valencia, Spain
- University of Florida, Institute of Food and Agricultural Sciences (IFAS), 2033 Mowry Road, Gainesville, FL, 32610, USA
| | | | - Christophe Delogne
- Technology, Data & Analytics, KPMG Luxembourg, Société Coopérative, 39 Avenue John F. Kennedy, 1855, Luxembourg, Luxembourg
| | - Peter Devilee
- Department of Human Genetics, Department of Pathology, Leiden University Medical Centre, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Alberto Di Meglio
- Information Technology Department, European Organization for Nuclear Research (CERN), 385 Route de Meyrin, 1211, Geneva 23, Switzerland
| | - Marinus Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Norbert Graf
- Department of Pediatric Oncology/Hematology, Saarland University, Campus Homburg, Building 9, 66421, Homburg, Germany
| | - Vera Grimm
- Project Management Jülich, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Yi-Ke Guo
- Data Science Institute, Imperial College London, South Kensington, London, SW7 2AZ, UK
| | - Ivo Glynne Gut
- CNAG-CRG, Center for Genomic Regulation, Barcelona Institute for Science and Technology (BIST), C/Baldiri Reixac 4, 08029, Barcelona, Spain
| | - Allan Hanbury
- Institute of Software Technology and Interactive Systems, TU Wien, Favoritenstrasse 9-11/188, 1040, Vienna, Austria
| | - Shahid Hanif
- The Association of the British Pharmaceutical Industry, 7th Floor, Southside, 105 Victoria Street, London, SW1E 6QT, UK
| | - Ralf-Dieter Hilgers
- Department of Medical Statistics, RWTH-Aachen University, Universitätsklinikum Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Ángel Honrado
- SYNAPSE Research Management Partners, Diputació 237, Àtic 3ª, 08007, Barcelona, Spain
| | - D Rod Hose
- Department of Infection, Immunity and Cardiovascular Disease and Insigneo Institute for In-Silico Medicine, Medical School, University of Sheffield, Beech Hill Road, Sheffield, S10 2RX, UK
| | | | - Tim Hubbard
- Department of Medical & Molecular Genetics, King's College London, London, SE1 9RT, UK
- Genomics England, London, EC1M 6BQ, UK
| | - Sophie Helen Janacek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Haralampos Karanikas
- National and Kapodistrian University of Athens, Medical School, Xristou Lada 6, 10561, Athens, Greece
| | - Tim Kievits
- Vitromics Healthcare Holding B.V., Onderwijsboulevard 225, 5223 DE, 's-Hertogenbosch, The Netherlands
| | - Manfred Kohler
- Fraunhofer Institute for Molecular Biology and Applied Ecology ScreeningPort, Schnackenburgallee 114, 22525, Hamburg, Germany
| | - Andreas Kremer
- ITTM S.A., 9 avenue des Hauts Fourneaux, 4362, Esch-sur-Alzette, Luxembourg
| | - Jerry Lanfear
- Research Business Technology, Pfizer Ltd, GP4 Building, Granta Park, Cambridge, CB21 6GP, UK
| | - Thomas Lengauer
- Max Planck Institute for Informatics, Campus E1 4, 66123, Saarbrücken, Germany
| | - Edith Maes
- Health Economics & Outcomes Research, Deloitte Belgium, Berkenlaan 8A, 1831, Diegem, Belgium
| | - Theo Meert
- Janssen Pharmaceutica N.V., R&D G3O, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Werner Müller
- Faculty of Life Sciences, University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, UK
| | - Dörthe Nickel
- UMR3664 IC/CNRS, Institut Curie, Section Recherche, Pavillon Pasteur, 26 rue d'Ulm, 75248, Paris cedex 05, France
| | - Peter Oledzki
- Linguamatics Ltd, 324 Cambridge Science Park Milton Rd, Cambridge, CB4 0WG, UK
| | - Bertrand Pedersen
- PwC Luxembourg, 2 rue Gerhard Mercator, 2182, Luxembourg, Luxembourg
| | - Milan Petkovic
- Philips, HighTechCampus 36, 5656AE, Eindhoven, The Netherlands
| | - Konstantinos Pliakos
- Department of Public Health and Primary Care, KU Leuven Kulak, Etienne Sabbelaan 53, 8500, Kortrijk, Belgium
| | - Magnus Rattray
- Faculty of Life Sciences, University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, UK
| | - Josep Redón I Màs
- INCLIVA Health Research Institute, University of Valencia, CIBERobn ISCIII, Avenida Menéndez Pelayo 4 accesorio, 46010, Valencia, Spain
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362, Esch-sur-Alzette, Luxembourg
| | - Thierry Sengstag
- Swiss Institute of Bioinformatics (SIB) and University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Xavier Serra-Picamal
- Agency for Health Quality and Assessment of Catalonia (AQuAS), Carrer de Roc Boronat 81-95, 08005, Barcelona, Spain
| | - Wouter Spek
- EuroBioForum Foundation, Chrysantstraat 10, 3135 HG, Vlaardingen, The Netherlands
| | - Lea A I Vaas
- Fraunhofer Institute for Molecular Biology and Applied Ecology ScreeningPort, Schnackenburgallee 114, 22525, Hamburg, Germany
| | - Okker van Batenburg
- EuroBioForum Foundation, Chrysantstraat 10, 3135 HG, Vlaardingen, The Netherlands
| | - Marc Vandelaer
- Integrated BioBank of Luxembourg, 6 rue Nicolas-Ernest Barblé, 1210, Luxembourg, Luxembourg
| | - Peter Varnai
- Technopolis Group, 3 Pavilion Buildings, Brighton, BN1 1EE, UK
| | - Pablo Villoslada
- Hospital Clinic of Barcelona, Institute d'Investigacions Biomediques August Pi Sunyer (IDIBAPS), Rosello 149, 08036, Barcelona, Spain
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - John Peter Mary Wubbe
- European Platform for Patients' Organisations, Science and Industry (Epposi), De Meeûs Square 38-40, 1000, Brussels, Belgium
| | - Gianluigi Zanetti
- CRS4, Ed.1 POLARIS, 09129, Pula, Italy
- BBMRI-ERIC, Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
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